[chore] remove vendor

10 files changed, 0 insertions, 1395 deletions

diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/aggregate.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/aggregate.go
deleted file mode 100644
index fde219333..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/aggregate.go
+++ /dev/null
@@ -1,153 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
-
-import (
-	"context"
-	"time"
-
-	"go.opentelemetry.io/otel/attribute"
-	"go.opentelemetry.io/otel/sdk/metric/metricdata"
-)
-
-// now is used to return the current local time while allowing tests to
-// override the default time.Now function.
-var now = time.Now
-
-// Measure receives measurements to be aggregated.
-type Measure[N int64 | float64] func(context.Context, N, attribute.Set)
-
-// ComputeAggregation stores the aggregate of measurements into dest and
-// returns the number of aggregate data-points output.
-type ComputeAggregation func(dest *metricdata.Aggregation) int
-
-// Builder builds an aggregate function.
-type Builder[N int64 | float64] struct {
-	// Temporality is the temporality used for the returned aggregate function.
-	//
-	// If this is not provided a default of cumulative will be used (except for
-	// the last-value aggregate function where delta is the only appropriate
-	// temporality).
-	Temporality metricdata.Temporality
-	// Filter is the attribute filter the aggregate function will use on the
-	// input of measurements.
-	Filter attribute.Filter
-	// ReservoirFunc is the factory function used by aggregate functions to
-	// create new exemplar reservoirs for a new seen attribute set.
-	//
-	// If this is not provided a default factory function that returns an
-	// dropReservoir reservoir will be used.
-	ReservoirFunc func(attribute.Set) FilteredExemplarReservoir[N]
-	// AggregationLimit is the cardinality limit of measurement attributes. Any
-	// measurement for new attributes once the limit has been reached will be
-	// aggregated into a single aggregate for the "otel.metric.overflow"
-	// attribute.
-	//
-	// If AggregationLimit is less than or equal to zero there will not be an
-	// aggregation limit imposed (i.e. unlimited attribute sets).
-	AggregationLimit int
-}
-
-func (b Builder[N]) resFunc() func(attribute.Set) FilteredExemplarReservoir[N] {
-	if b.ReservoirFunc != nil {
-		return b.ReservoirFunc
-	}
-
-	return dropReservoir
-}
-
-type fltrMeasure[N int64 | float64] func(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue)
-
-func (b Builder[N]) filter(f fltrMeasure[N]) Measure[N] {
-	if b.Filter != nil {
-		fltr := b.Filter // Copy to make it immutable after assignment.
-		return func(ctx context.Context, n N, a attribute.Set) {
-			fAttr, dropped := a.Filter(fltr)
-			f(ctx, n, fAttr, dropped)
-		}
-	}
-	return func(ctx context.Context, n N, a attribute.Set) {
-		f(ctx, n, a, nil)
-	}
-}
-
-// LastValue returns a last-value aggregate function input and output.
-func (b Builder[N]) LastValue() (Measure[N], ComputeAggregation) {
-	lv := newLastValue[N](b.AggregationLimit, b.resFunc())
-	switch b.Temporality {
-	case metricdata.DeltaTemporality:
-		return b.filter(lv.measure), lv.delta
-	default:
-		return b.filter(lv.measure), lv.cumulative
-	}
-}
-
-// PrecomputedLastValue returns a last-value aggregate function input and
-// output. The aggregation returned from the returned ComputeAggregation
-// function will always only return values from the previous collection cycle.
-func (b Builder[N]) PrecomputedLastValue() (Measure[N], ComputeAggregation) {
-	lv := newPrecomputedLastValue[N](b.AggregationLimit, b.resFunc())
-	switch b.Temporality {
-	case metricdata.DeltaTemporality:
-		return b.filter(lv.measure), lv.delta
-	default:
-		return b.filter(lv.measure), lv.cumulative
-	}
-}
-
-// PrecomputedSum returns a sum aggregate function input and output. The
-// arguments passed to the input are expected to be the precomputed sum values.
-func (b Builder[N]) PrecomputedSum(monotonic bool) (Measure[N], ComputeAggregation) {
-	s := newPrecomputedSum[N](monotonic, b.AggregationLimit, b.resFunc())
-	switch b.Temporality {
-	case metricdata.DeltaTemporality:
-		return b.filter(s.measure), s.delta
-	default:
-		return b.filter(s.measure), s.cumulative
-	}
-}
-
-// Sum returns a sum aggregate function input and output.
-func (b Builder[N]) Sum(monotonic bool) (Measure[N], ComputeAggregation) {
-	s := newSum[N](monotonic, b.AggregationLimit, b.resFunc())
-	switch b.Temporality {
-	case metricdata.DeltaTemporality:
-		return b.filter(s.measure), s.delta
-	default:
-		return b.filter(s.measure), s.cumulative
-	}
-}
-
-// ExplicitBucketHistogram returns a histogram aggregate function input and
-// output.
-func (b Builder[N]) ExplicitBucketHistogram(boundaries []float64, noMinMax, noSum bool) (Measure[N], ComputeAggregation) {
-	h := newHistogram[N](boundaries, noMinMax, noSum, b.AggregationLimit, b.resFunc())
-	switch b.Temporality {
-	case metricdata.DeltaTemporality:
-		return b.filter(h.measure), h.delta
-	default:
-		return b.filter(h.measure), h.cumulative
-	}
-}
-
-// ExponentialBucketHistogram returns a histogram aggregate function input and
-// output.
-func (b Builder[N]) ExponentialBucketHistogram(maxSize, maxScale int32, noMinMax, noSum bool) (Measure[N], ComputeAggregation) {
-	h := newExponentialHistogram[N](maxSize, maxScale, noMinMax, noSum, b.AggregationLimit, b.resFunc())
-	switch b.Temporality {
-	case metricdata.DeltaTemporality:
-		return b.filter(h.measure), h.delta
-	default:
-		return b.filter(h.measure), h.cumulative
-	}
-}
-
-// reset ensures s has capacity and sets it length. If the capacity of s too
-// small, a new slice is returned with the specified capacity and length.
-func reset[T any](s []T, length, capacity int) []T {
-	if cap(s) < capacity {
-		return make([]T, length, capacity)
-	}
-	return s[:length]
-}
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/doc.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/doc.go
deleted file mode 100644
index 7b7225e6e..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/doc.go
+++ /dev/null
@@ -1,7 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-// Package aggregate provides aggregate types used compute aggregations and
-// cycle the state of metric measurements made by the SDK. These types and
-// functionality are meant only for internal SDK use.
-package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/drop.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/drop.go
deleted file mode 100644
index 8396faaa4..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/drop.go
+++ /dev/null
@@ -1,27 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
-
-import (
-	"context"
-
-	"go.opentelemetry.io/otel/attribute"
-	"go.opentelemetry.io/otel/sdk/metric/exemplar"
-)
-
-// dropReservoir returns a [FilteredReservoir] that drops all measurements it is offered.
-func dropReservoir[N int64 | float64](attribute.Set) FilteredExemplarReservoir[N] {
-	return &dropRes[N]{}
-}
-
-type dropRes[N int64 | float64] struct{}
-
-// Offer does nothing, all measurements offered will be dropped.
-func (r *dropRes[N]) Offer(context.Context, N, []attribute.KeyValue) {}
-
-// Collect resets dest. No exemplars will ever be returned.
-func (r *dropRes[N]) Collect(dest *[]exemplar.Exemplar) {
-	clear(*dest) // Erase elements to let GC collect objects
-	*dest = (*dest)[:0]
-}
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exemplar.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exemplar.go
deleted file mode 100644
index 25d709948..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exemplar.go
+++ /dev/null
@@ -1,43 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
-
-import (
-	"sync"
-
-	"go.opentelemetry.io/otel/sdk/metric/exemplar"
-	"go.opentelemetry.io/otel/sdk/metric/metricdata"
-)
-
-var exemplarPool = sync.Pool{
-	New: func() any { return new([]exemplar.Exemplar) },
-}
-
-func collectExemplars[N int64 | float64](out *[]metricdata.Exemplar[N], f func(*[]exemplar.Exemplar)) {
-	dest := exemplarPool.Get().(*[]exemplar.Exemplar)
-	defer func() {
-		clear(*dest) // Erase elements to let GC collect objects.
-		*dest = (*dest)[:0]
-		exemplarPool.Put(dest)
-	}()
-
-	*dest = reset(*dest, len(*out), cap(*out))
-
-	f(dest)
-
-	*out = reset(*out, len(*dest), cap(*dest))
-	for i, e := range *dest {
-		(*out)[i].FilteredAttributes = e.FilteredAttributes
-		(*out)[i].Time = e.Time
-		(*out)[i].SpanID = e.SpanID
-		(*out)[i].TraceID = e.TraceID
-
-		switch e.Value.Type() {
-		case exemplar.Int64ValueType:
-			(*out)[i].Value = N(e.Value.Int64())
-		case exemplar.Float64ValueType:
-			(*out)[i].Value = N(e.Value.Float64())
-		}
-	}
-}
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go
deleted file mode 100644
index 336ea91d1..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/exponential_histogram.go
+++ /dev/null
@@ -1,443 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
-
-import (
-	"context"
-	"errors"
-	"math"
-	"sync"
-	"time"
-
-	"go.opentelemetry.io/otel"
-	"go.opentelemetry.io/otel/attribute"
-	"go.opentelemetry.io/otel/sdk/metric/metricdata"
-)
-
-const (
-	expoMaxScale = 20
-	expoMinScale = -10
-
-	smallestNonZeroNormalFloat64 = 0x1p-1022
-
-	// These redefine the Math constants with a type, so the compiler won't coerce
-	// them into an int on 32 bit platforms.
-	maxInt64 int64 = math.MaxInt64
-	minInt64 int64 = math.MinInt64
-)
-
-// expoHistogramDataPoint is a single data point in an exponential histogram.
-type expoHistogramDataPoint[N int64 | float64] struct {
-	attrs attribute.Set
-	res   FilteredExemplarReservoir[N]
-
-	count uint64
-	min   N
-	max   N
-	sum   N
-
-	maxSize  int
-	noMinMax bool
-	noSum    bool
-
-	scale int32
-
-	posBuckets expoBuckets
-	negBuckets expoBuckets
-	zeroCount  uint64
-}
-
-func newExpoHistogramDataPoint[N int64 | float64](attrs attribute.Set, maxSize int, maxScale int32, noMinMax, noSum bool) *expoHistogramDataPoint[N] {
-	f := math.MaxFloat64
-	ma := N(f) // if N is int64, max will overflow to -9223372036854775808
-	mi := N(-f)
-	if N(maxInt64) > N(f) {
-		ma = N(maxInt64)
-		mi = N(minInt64)
-	}
-	return &expoHistogramDataPoint[N]{
-		attrs:    attrs,
-		min:      ma,
-		max:      mi,
-		maxSize:  maxSize,
-		noMinMax: noMinMax,
-		noSum:    noSum,
-		scale:    maxScale,
-	}
-}
-
-// record adds a new measurement to the histogram. It will rescale the buckets if needed.
-func (p *expoHistogramDataPoint[N]) record(v N) {
-	p.count++
-
-	if !p.noMinMax {
-		if v < p.min {
-			p.min = v
-		}
-		if v > p.max {
-			p.max = v
-		}
-	}
-	if !p.noSum {
-		p.sum += v
-	}
-
-	absV := math.Abs(float64(v))
-
-	if float64(absV) == 0.0 {
-		p.zeroCount++
-		return
-	}
-
-	bin := p.getBin(absV)
-
-	bucket := &p.posBuckets
-	if v < 0 {
-		bucket = &p.negBuckets
-	}
-
-	// If the new bin would make the counts larger than maxScale, we need to
-	// downscale current measurements.
-	if scaleDelta := p.scaleChange(bin, bucket.startBin, len(bucket.counts)); scaleDelta > 0 {
-		if p.scale-scaleDelta < expoMinScale {
-			// With a scale of -10 there is only two buckets for the whole range of float64 values.
-			// This can only happen if there is a max size of 1.
-			otel.Handle(errors.New("exponential histogram scale underflow"))
-			return
-		}
-		// Downscale
-		p.scale -= scaleDelta
-		p.posBuckets.downscale(scaleDelta)
-		p.negBuckets.downscale(scaleDelta)
-
-		bin = p.getBin(absV)
-	}
-
-	bucket.record(bin)
-}
-
-// getBin returns the bin v should be recorded into.
-func (p *expoHistogramDataPoint[N]) getBin(v float64) int32 {
-	frac, expInt := math.Frexp(v)
-	// 11-bit exponential.
-	exp := int32(expInt) // nolint: gosec
-	if p.scale <= 0 {
-		// Because of the choice of fraction is always 1 power of two higher than we want.
-		var correction int32 = 1
-		if frac == .5 {
-			// If v is an exact power of two the frac will be .5 and the exp
-			// will be one higher than we want.
-			correction = 2
-		}
-		return (exp - correction) >> (-p.scale)
-	}
-	return exp<<p.scale + int32(math.Log(frac)*scaleFactors[p.scale]) - 1
-}
-
-// scaleFactors are constants used in calculating the logarithm index. They are
-// equivalent to 2^index/log(2).
-var scaleFactors = [21]float64{
-	math.Ldexp(math.Log2E, 0),
-	math.Ldexp(math.Log2E, 1),
-	math.Ldexp(math.Log2E, 2),
-	math.Ldexp(math.Log2E, 3),
-	math.Ldexp(math.Log2E, 4),
-	math.Ldexp(math.Log2E, 5),
-	math.Ldexp(math.Log2E, 6),
-	math.Ldexp(math.Log2E, 7),
-	math.Ldexp(math.Log2E, 8),
-	math.Ldexp(math.Log2E, 9),
-	math.Ldexp(math.Log2E, 10),
-	math.Ldexp(math.Log2E, 11),
-	math.Ldexp(math.Log2E, 12),
-	math.Ldexp(math.Log2E, 13),
-	math.Ldexp(math.Log2E, 14),
-	math.Ldexp(math.Log2E, 15),
-	math.Ldexp(math.Log2E, 16),
-	math.Ldexp(math.Log2E, 17),
-	math.Ldexp(math.Log2E, 18),
-	math.Ldexp(math.Log2E, 19),
-	math.Ldexp(math.Log2E, 20),
-}
-
-// scaleChange returns the magnitude of the scale change needed to fit bin in
-// the bucket. If no scale change is needed 0 is returned.
-func (p *expoHistogramDataPoint[N]) scaleChange(bin, startBin int32, length int) int32 {
-	if length == 0 {
-		// No need to rescale if there are no buckets.
-		return 0
-	}
-
-	low := int(startBin)
-	high := int(bin)
-	if startBin >= bin {
-		low = int(bin)
-		high = int(startBin) + length - 1
-	}
-
-	var count int32
-	for high-low >= p.maxSize {
-		low = low >> 1
-		high = high >> 1
-		count++
-		if count > expoMaxScale-expoMinScale {
-			return count
-		}
-	}
-	return count
-}
-
-// expoBuckets is a set of buckets in an exponential histogram.
-type expoBuckets struct {
-	startBin int32
-	counts   []uint64
-}
-
-// record increments the count for the given bin, and expands the buckets if needed.
-// Size changes must be done before calling this function.
-func (b *expoBuckets) record(bin int32) {
-	if len(b.counts) == 0 {
-		b.counts = []uint64{1}
-		b.startBin = bin
-		return
-	}
-
-	endBin := int(b.startBin) + len(b.counts) - 1
-
-	// if the new bin is inside the current range
-	if bin >= b.startBin && int(bin) <= endBin {
-		b.counts[bin-b.startBin]++
-		return
-	}
-	// if the new bin is before the current start add spaces to the counts
-	if bin < b.startBin {
-		origLen := len(b.counts)
-		newLength := endBin - int(bin) + 1
-		shift := b.startBin - bin
-
-		if newLength > cap(b.counts) {
-			b.counts = append(b.counts, make([]uint64, newLength-len(b.counts))...)
-		}
-
-		copy(b.counts[shift:origLen+int(shift)], b.counts[:])
-		b.counts = b.counts[:newLength]
-		for i := 1; i < int(shift); i++ {
-			b.counts[i] = 0
-		}
-		b.startBin = bin
-		b.counts[0] = 1
-		return
-	}
-	// if the new is after the end add spaces to the end
-	if int(bin) > endBin {
-		if int(bin-b.startBin) < cap(b.counts) {
-			b.counts = b.counts[:bin-b.startBin+1]
-			for i := endBin + 1 - int(b.startBin); i < len(b.counts); i++ {
-				b.counts[i] = 0
-			}
-			b.counts[bin-b.startBin] = 1
-			return
-		}
-
-		end := make([]uint64, int(bin-b.startBin)-len(b.counts)+1)
-		b.counts = append(b.counts, end...)
-		b.counts[bin-b.startBin] = 1
-	}
-}
-
-// downscale shrinks a bucket by a factor of 2*s. It will sum counts into the
-// correct lower resolution bucket.
-func (b *expoBuckets) downscale(delta int32) {
-	// Example
-	// delta = 2
-	// Original offset: -6
-	// Counts: [ 3,  1,  2,  3,  4,  5, 6, 7, 8, 9, 10]
-	// bins:    -6  -5, -4, -3, -2, -1, 0, 1, 2, 3, 4
-	// new bins:-2, -2, -1, -1, -1, -1, 0, 0, 0, 0, 1
-	// new Offset: -2
-	// new Counts: [4, 14, 30, 10]
-
-	if len(b.counts) <= 1 || delta < 1 {
-		b.startBin = b.startBin >> delta
-		return
-	}
-
-	steps := int32(1) << delta
-	offset := b.startBin % steps
-	offset = (offset + steps) % steps // to make offset positive
-	for i := 1; i < len(b.counts); i++ {
-		idx := i + int(offset)
-		if idx%int(steps) == 0 {
-			b.counts[idx/int(steps)] = b.counts[i]
-			continue
-		}
-		b.counts[idx/int(steps)] += b.counts[i]
-	}
-
-	lastIdx := (len(b.counts) - 1 + int(offset)) / int(steps)
-	b.counts = b.counts[:lastIdx+1]
-	b.startBin = b.startBin >> delta
-}
-
-// newExponentialHistogram returns an Aggregator that summarizes a set of
-// measurements as an exponential histogram. Each histogram is scoped by attributes
-// and the aggregation cycle the measurements were made in.
-func newExponentialHistogram[N int64 | float64](maxSize, maxScale int32, noMinMax, noSum bool, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *expoHistogram[N] {
-	return &expoHistogram[N]{
-		noSum:    noSum,
-		noMinMax: noMinMax,
-		maxSize:  int(maxSize),
-		maxScale: maxScale,
-
-		newRes: r,
-		limit:  newLimiter[*expoHistogramDataPoint[N]](limit),
-		values: make(map[attribute.Distinct]*expoHistogramDataPoint[N]),
-
-		start: now(),
-	}
-}
-
-// expoHistogram summarizes a set of measurements as an histogram with exponentially
-// defined buckets.
-type expoHistogram[N int64 | float64] struct {
-	noSum    bool
-	noMinMax bool
-	maxSize  int
-	maxScale int32
-
-	newRes   func(attribute.Set) FilteredExemplarReservoir[N]
-	limit    limiter[*expoHistogramDataPoint[N]]
-	values   map[attribute.Distinct]*expoHistogramDataPoint[N]
-	valuesMu sync.Mutex
-
-	start time.Time
-}
-
-func (e *expoHistogram[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) {
-	// Ignore NaN and infinity.
-	if math.IsInf(float64(value), 0) || math.IsNaN(float64(value)) {
-		return
-	}
-
-	e.valuesMu.Lock()
-	defer e.valuesMu.Unlock()
-
-	attr := e.limit.Attributes(fltrAttr, e.values)
-	v, ok := e.values[attr.Equivalent()]
-	if !ok {
-		v = newExpoHistogramDataPoint[N](attr, e.maxSize, e.maxScale, e.noMinMax, e.noSum)
-		v.res = e.newRes(attr)
-
-		e.values[attr.Equivalent()] = v
-	}
-	v.record(value)
-	v.res.Offer(ctx, value, droppedAttr)
-}
-
-func (e *expoHistogram[N]) delta(dest *metricdata.Aggregation) int {
-	t := now()
-
-	// If *dest is not a metricdata.ExponentialHistogram, memory reuse is missed.
-	// In that case, use the zero-value h and hope for better alignment next cycle.
-	h, _ := (*dest).(metricdata.ExponentialHistogram[N])
-	h.Temporality = metricdata.DeltaTemporality
-
-	e.valuesMu.Lock()
-	defer e.valuesMu.Unlock()
-
-	n := len(e.values)
-	hDPts := reset(h.DataPoints, n, n)
-
-	var i int
-	for _, val := range e.values {
-		hDPts[i].Attributes = val.attrs
-		hDPts[i].StartTime = e.start
-		hDPts[i].Time = t
-		hDPts[i].Count = val.count
-		hDPts[i].Scale = val.scale
-		hDPts[i].ZeroCount = val.zeroCount
-		hDPts[i].ZeroThreshold = 0.0
-
-		hDPts[i].PositiveBucket.Offset = val.posBuckets.startBin
-		hDPts[i].PositiveBucket.Counts = reset(hDPts[i].PositiveBucket.Counts, len(val.posBuckets.counts), len(val.posBuckets.counts))
-		copy(hDPts[i].PositiveBucket.Counts, val.posBuckets.counts)
-
-		hDPts[i].NegativeBucket.Offset = val.negBuckets.startBin
-		hDPts[i].NegativeBucket.Counts = reset(hDPts[i].NegativeBucket.Counts, len(val.negBuckets.counts), len(val.negBuckets.counts))
-		copy(hDPts[i].NegativeBucket.Counts, val.negBuckets.counts)
-
-		if !e.noSum {
-			hDPts[i].Sum = val.sum
-		}
-		if !e.noMinMax {
-			hDPts[i].Min = metricdata.NewExtrema(val.min)
-			hDPts[i].Max = metricdata.NewExtrema(val.max)
-		}
-
-		collectExemplars(&hDPts[i].Exemplars, val.res.Collect)
-
-		i++
-	}
-	// Unused attribute sets do not report.
-	clear(e.values)
-
-	e.start = t
-	h.DataPoints = hDPts
-	*dest = h
-	return n
-}
-
-func (e *expoHistogram[N]) cumulative(dest *metricdata.Aggregation) int {
-	t := now()
-
-	// If *dest is not a metricdata.ExponentialHistogram, memory reuse is missed.
-	// In that case, use the zero-value h and hope for better alignment next cycle.
-	h, _ := (*dest).(metricdata.ExponentialHistogram[N])
-	h.Temporality = metricdata.CumulativeTemporality
-
-	e.valuesMu.Lock()
-	defer e.valuesMu.Unlock()
-
-	n := len(e.values)
-	hDPts := reset(h.DataPoints, n, n)
-
-	var i int
-	for _, val := range e.values {
-		hDPts[i].Attributes = val.attrs
-		hDPts[i].StartTime = e.start
-		hDPts[i].Time = t
-		hDPts[i].Count = val.count
-		hDPts[i].Scale = val.scale
-		hDPts[i].ZeroCount = val.zeroCount
-		hDPts[i].ZeroThreshold = 0.0
-
-		hDPts[i].PositiveBucket.Offset = val.posBuckets.startBin
-		hDPts[i].PositiveBucket.Counts = reset(hDPts[i].PositiveBucket.Counts, len(val.posBuckets.counts), len(val.posBuckets.counts))
-		copy(hDPts[i].PositiveBucket.Counts, val.posBuckets.counts)
-
-		hDPts[i].NegativeBucket.Offset = val.negBuckets.startBin
-		hDPts[i].NegativeBucket.Counts = reset(hDPts[i].NegativeBucket.Counts, len(val.negBuckets.counts), len(val.negBuckets.counts))
-		copy(hDPts[i].NegativeBucket.Counts, val.negBuckets.counts)
-
-		if !e.noSum {
-			hDPts[i].Sum = val.sum
-		}
-		if !e.noMinMax {
-			hDPts[i].Min = metricdata.NewExtrema(val.min)
-			hDPts[i].Max = metricdata.NewExtrema(val.max)
-		}
-
-		collectExemplars(&hDPts[i].Exemplars, val.res.Collect)
-
-		i++
-		// TODO (#3006): This will use an unbounded amount of memory if there
-		// are unbounded number of attribute sets being aggregated. Attribute
-		// sets that become "stale" need to be forgotten so this will not
-		// overload the system.
-	}
-
-	h.DataPoints = hDPts
-	*dest = h
-	return n
-}
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/filtered_reservoir.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/filtered_reservoir.go
deleted file mode 100644
index 691a91060..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/filtered_reservoir.go
+++ /dev/null
@@ -1,50 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
-
-import (
-	"context"
-	"time"
-
-	"go.opentelemetry.io/otel/attribute"
-	"go.opentelemetry.io/otel/sdk/metric/exemplar"
-)
-
-// FilteredExemplarReservoir wraps a [exemplar.Reservoir] with a filter.
-type FilteredExemplarReservoir[N int64 | float64] interface {
-	// Offer accepts the parameters associated with a measurement. The
-	// parameters will be stored as an exemplar if the filter decides to
-	// sample the measurement.
-	//
-	// The passed ctx needs to contain any baggage or span that were active
-	// when the measurement was made. This information may be used by the
-	// Reservoir in making a sampling decision.
-	Offer(ctx context.Context, val N, attr []attribute.KeyValue)
-	// Collect returns all the held exemplars in the reservoir.
-	Collect(dest *[]exemplar.Exemplar)
-}
-
-// filteredExemplarReservoir handles the pre-sampled exemplar of measurements made.
-type filteredExemplarReservoir[N int64 | float64] struct {
-	filter    exemplar.Filter
-	reservoir exemplar.Reservoir
-}
-
-// NewFilteredExemplarReservoir creates a [FilteredExemplarReservoir] which only offers values
-// that are allowed by the filter.
-func NewFilteredExemplarReservoir[N int64 | float64](f exemplar.Filter, r exemplar.Reservoir) FilteredExemplarReservoir[N] {
-	return &filteredExemplarReservoir[N]{
-		filter:    f,
-		reservoir: r,
-	}
-}
-
-func (f *filteredExemplarReservoir[N]) Offer(ctx context.Context, val N, attr []attribute.KeyValue) {
-	if f.filter(ctx) {
-		// only record the current time if we are sampling this measurement.
-		f.reservoir.Offer(ctx, time.Now(), exemplar.NewValue(val), attr)
-	}
-}
-
-func (f *filteredExemplarReservoir[N]) Collect(dest *[]exemplar.Exemplar) { f.reservoir.Collect(dest) }
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go
deleted file mode 100644
index d577ae2c1..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/histogram.go
+++ /dev/null
@@ -1,232 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
-
-import (
-	"context"
-	"slices"
-	"sort"
-	"sync"
-	"time"
-
-	"go.opentelemetry.io/otel/attribute"
-	"go.opentelemetry.io/otel/sdk/metric/metricdata"
-)
-
-type buckets[N int64 | float64] struct {
-	attrs attribute.Set
-	res   FilteredExemplarReservoir[N]
-
-	counts   []uint64
-	count    uint64
-	total    N
-	min, max N
-}
-
-// newBuckets returns buckets with n bins.
-func newBuckets[N int64 | float64](attrs attribute.Set, n int) *buckets[N] {
-	return &buckets[N]{attrs: attrs, counts: make([]uint64, n)}
-}
-
-func (b *buckets[N]) sum(value N) { b.total += value }
-
-func (b *buckets[N]) bin(idx int, value N) {
-	b.counts[idx]++
-	b.count++
-	if value < b.min {
-		b.min = value
-	} else if value > b.max {
-		b.max = value
-	}
-}
-
-// histValues summarizes a set of measurements as an histValues with
-// explicitly defined buckets.
-type histValues[N int64 | float64] struct {
-	noSum  bool
-	bounds []float64
-
-	newRes   func(attribute.Set) FilteredExemplarReservoir[N]
-	limit    limiter[*buckets[N]]
-	values   map[attribute.Distinct]*buckets[N]
-	valuesMu sync.Mutex
-}
-
-func newHistValues[N int64 | float64](bounds []float64, noSum bool, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *histValues[N] {
-	// The responsibility of keeping all buckets correctly associated with the
-	// passed boundaries is ultimately this type's responsibility. Make a copy
-	// here so we can always guarantee this. Or, in the case of failure, have
-	// complete control over the fix.
-	b := slices.Clone(bounds)
-	slices.Sort(b)
-	return &histValues[N]{
-		noSum:  noSum,
-		bounds: b,
-		newRes: r,
-		limit:  newLimiter[*buckets[N]](limit),
-		values: make(map[attribute.Distinct]*buckets[N]),
-	}
-}
-
-// Aggregate records the measurement value, scoped by attr, and aggregates it
-// into a histogram.
-func (s *histValues[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) {
-	// This search will return an index in the range [0, len(s.bounds)], where
-	// it will return len(s.bounds) if value is greater than the last element
-	// of s.bounds. This aligns with the buckets in that the length of buckets
-	// is len(s.bounds)+1, with the last bucket representing:
-	// (s.bounds[len(s.bounds)-1], +∞).
-	idx := sort.SearchFloat64s(s.bounds, float64(value))
-
-	s.valuesMu.Lock()
-	defer s.valuesMu.Unlock()
-
-	attr := s.limit.Attributes(fltrAttr, s.values)
-	b, ok := s.values[attr.Equivalent()]
-	if !ok {
-		// N+1 buckets. For example:
-		//
-		//   bounds = [0, 5, 10]
-		//
-		// Then,
-		//
-		//   buckets = (-∞, 0], (0, 5.0], (5.0, 10.0], (10.0, +∞)
-		b = newBuckets[N](attr, len(s.bounds)+1)
-		b.res = s.newRes(attr)
-
-		// Ensure min and max are recorded values (not zero), for new buckets.
-		b.min, b.max = value, value
-		s.values[attr.Equivalent()] = b
-	}
-	b.bin(idx, value)
-	if !s.noSum {
-		b.sum(value)
-	}
-	b.res.Offer(ctx, value, droppedAttr)
-}
-
-// newHistogram returns an Aggregator that summarizes a set of measurements as
-// an histogram.
-func newHistogram[N int64 | float64](boundaries []float64, noMinMax, noSum bool, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *histogram[N] {
-	return &histogram[N]{
-		histValues: newHistValues[N](boundaries, noSum, limit, r),
-		noMinMax:   noMinMax,
-		start:      now(),
-	}
-}
-
-// histogram summarizes a set of measurements as an histogram with explicitly
-// defined buckets.
-type histogram[N int64 | float64] struct {
-	*histValues[N]
-
-	noMinMax bool
-	start    time.Time
-}
-
-func (s *histogram[N]) delta(dest *metricdata.Aggregation) int {
-	t := now()
-
-	// If *dest is not a metricdata.Histogram, memory reuse is missed. In that
-	// case, use the zero-value h and hope for better alignment next cycle.
-	h, _ := (*dest).(metricdata.Histogram[N])
-	h.Temporality = metricdata.DeltaTemporality
-
-	s.valuesMu.Lock()
-	defer s.valuesMu.Unlock()
-
-	// Do not allow modification of our copy of bounds.
-	bounds := slices.Clone(s.bounds)
-
-	n := len(s.values)
-	hDPts := reset(h.DataPoints, n, n)
-
-	var i int
-	for _, val := range s.values {
-		hDPts[i].Attributes = val.attrs
-		hDPts[i].StartTime = s.start
-		hDPts[i].Time = t
-		hDPts[i].Count = val.count
-		hDPts[i].Bounds = bounds
-		hDPts[i].BucketCounts = val.counts
-
-		if !s.noSum {
-			hDPts[i].Sum = val.total
-		}
-
-		if !s.noMinMax {
-			hDPts[i].Min = metricdata.NewExtrema(val.min)
-			hDPts[i].Max = metricdata.NewExtrema(val.max)
-		}
-
-		collectExemplars(&hDPts[i].Exemplars, val.res.Collect)
-
-		i++
-	}
-	// Unused attribute sets do not report.
-	clear(s.values)
-	// The delta collection cycle resets.
-	s.start = t
-
-	h.DataPoints = hDPts
-	*dest = h
-
-	return n
-}
-
-func (s *histogram[N]) cumulative(dest *metricdata.Aggregation) int {
-	t := now()
-
-	// If *dest is not a metricdata.Histogram, memory reuse is missed. In that
-	// case, use the zero-value h and hope for better alignment next cycle.
-	h, _ := (*dest).(metricdata.Histogram[N])
-	h.Temporality = metricdata.CumulativeTemporality
-
-	s.valuesMu.Lock()
-	defer s.valuesMu.Unlock()
-
-	// Do not allow modification of our copy of bounds.
-	bounds := slices.Clone(s.bounds)
-
-	n := len(s.values)
-	hDPts := reset(h.DataPoints, n, n)
-
-	var i int
-	for _, val := range s.values {
-		hDPts[i].Attributes = val.attrs
-		hDPts[i].StartTime = s.start
-		hDPts[i].Time = t
-		hDPts[i].Count = val.count
-		hDPts[i].Bounds = bounds
-
-		// The HistogramDataPoint field values returned need to be copies of
-		// the buckets value as we will keep updating them.
-		//
-		// TODO (#3047): Making copies for bounds and counts incurs a large
-		// memory allocation footprint. Alternatives should be explored.
-		hDPts[i].BucketCounts = slices.Clone(val.counts)
-
-		if !s.noSum {
-			hDPts[i].Sum = val.total
-		}
-
-		if !s.noMinMax {
-			hDPts[i].Min = metricdata.NewExtrema(val.min)
-			hDPts[i].Max = metricdata.NewExtrema(val.max)
-		}
-
-		collectExemplars(&hDPts[i].Exemplars, val.res.Collect)
-
-		i++
-		// TODO (#3006): This will use an unbounded amount of memory if there
-		// are unbounded number of attribute sets being aggregated. Attribute
-		// sets that become "stale" need to be forgotten so this will not
-		// overload the system.
-	}
-
-	h.DataPoints = hDPts
-	*dest = h
-
-	return n
-}
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go
deleted file mode 100644
index d3a93f085..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/lastvalue.go
+++ /dev/null
@@ -1,161 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
-
-import (
-	"context"
-	"sync"
-	"time"
-
-	"go.opentelemetry.io/otel/attribute"
-	"go.opentelemetry.io/otel/sdk/metric/metricdata"
-)
-
-// datapoint is timestamped measurement data.
-type datapoint[N int64 | float64] struct {
-	attrs attribute.Set
-	value N
-	res   FilteredExemplarReservoir[N]
-}
-
-func newLastValue[N int64 | float64](limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *lastValue[N] {
-	return &lastValue[N]{
-		newRes: r,
-		limit:  newLimiter[datapoint[N]](limit),
-		values: make(map[attribute.Distinct]datapoint[N]),
-		start:  now(),
-	}
-}
-
-// lastValue summarizes a set of measurements as the last one made.
-type lastValue[N int64 | float64] struct {
-	sync.Mutex
-
-	newRes func(attribute.Set) FilteredExemplarReservoir[N]
-	limit  limiter[datapoint[N]]
-	values map[attribute.Distinct]datapoint[N]
-	start  time.Time
-}
-
-func (s *lastValue[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) {
-	s.Lock()
-	defer s.Unlock()
-
-	attr := s.limit.Attributes(fltrAttr, s.values)
-	d, ok := s.values[attr.Equivalent()]
-	if !ok {
-		d.res = s.newRes(attr)
-	}
-
-	d.attrs = attr
-	d.value = value
-	d.res.Offer(ctx, value, droppedAttr)
-
-	s.values[attr.Equivalent()] = d
-}
-
-func (s *lastValue[N]) delta(dest *metricdata.Aggregation) int {
-	t := now()
-	// Ignore if dest is not a metricdata.Gauge. The chance for memory reuse of
-	// the DataPoints is missed (better luck next time).
-	gData, _ := (*dest).(metricdata.Gauge[N])
-
-	s.Lock()
-	defer s.Unlock()
-
-	n := s.copyDpts(&gData.DataPoints, t)
-	// Do not report stale values.
-	clear(s.values)
-	// Update start time for delta temporality.
-	s.start = t
-
-	*dest = gData
-
-	return n
-}
-
-func (s *lastValue[N]) cumulative(dest *metricdata.Aggregation) int {
-	t := now()
-	// Ignore if dest is not a metricdata.Gauge. The chance for memory reuse of
-	// the DataPoints is missed (better luck next time).
-	gData, _ := (*dest).(metricdata.Gauge[N])
-
-	s.Lock()
-	defer s.Unlock()
-
-	n := s.copyDpts(&gData.DataPoints, t)
-	// TODO (#3006): This will use an unbounded amount of memory if there
-	// are unbounded number of attribute sets being aggregated. Attribute
-	// sets that become "stale" need to be forgotten so this will not
-	// overload the system.
-	*dest = gData
-
-	return n
-}
-
-// copyDpts copies the datapoints held by s into dest. The number of datapoints
-// copied is returned.
-func (s *lastValue[N]) copyDpts(dest *[]metricdata.DataPoint[N], t time.Time) int {
-	n := len(s.values)
-	*dest = reset(*dest, n, n)
-
-	var i int
-	for _, v := range s.values {
-		(*dest)[i].Attributes = v.attrs
-		(*dest)[i].StartTime = s.start
-		(*dest)[i].Time = t
-		(*dest)[i].Value = v.value
-		collectExemplars(&(*dest)[i].Exemplars, v.res.Collect)
-		i++
-	}
-	return n
-}
-
-// newPrecomputedLastValue returns an aggregator that summarizes a set of
-// observations as the last one made.
-func newPrecomputedLastValue[N int64 | float64](limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *precomputedLastValue[N] {
-	return &precomputedLastValue[N]{lastValue: newLastValue[N](limit, r)}
-}
-
-// precomputedLastValue summarizes a set of observations as the last one made.
-type precomputedLastValue[N int64 | float64] struct {
-	*lastValue[N]
-}
-
-func (s *precomputedLastValue[N]) delta(dest *metricdata.Aggregation) int {
-	t := now()
-	// Ignore if dest is not a metricdata.Gauge. The chance for memory reuse of
-	// the DataPoints is missed (better luck next time).
-	gData, _ := (*dest).(metricdata.Gauge[N])
-
-	s.Lock()
-	defer s.Unlock()
-
-	n := s.copyDpts(&gData.DataPoints, t)
-	// Do not report stale values.
-	clear(s.values)
-	// Update start time for delta temporality.
-	s.start = t
-
-	*dest = gData
-
-	return n
-}
-
-func (s *precomputedLastValue[N]) cumulative(dest *metricdata.Aggregation) int {
-	t := now()
-	// Ignore if dest is not a metricdata.Gauge. The chance for memory reuse of
-	// the DataPoints is missed (better luck next time).
-	gData, _ := (*dest).(metricdata.Gauge[N])
-
-	s.Lock()
-	defer s.Unlock()
-
-	n := s.copyDpts(&gData.DataPoints, t)
-	// Do not report stale values.
-	clear(s.values)
-	*dest = gData
-
-	return n
-}
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/limit.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/limit.go
deleted file mode 100644
index 9ea0251ed..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/limit.go
+++ /dev/null
@@ -1,42 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
-
-import "go.opentelemetry.io/otel/attribute"
-
-// overflowSet is the attribute set used to record a measurement when adding
-// another distinct attribute set to the aggregate would exceed the aggregate
-// limit.
-var overflowSet = attribute.NewSet(attribute.Bool("otel.metric.overflow", true))
-
-// limiter limits aggregate values.
-type limiter[V any] struct {
-	// aggLimit is the maximum number of metric streams that can be aggregated.
-	//
-	// Any metric stream with attributes distinct from any set already
-	// aggregated once the aggLimit will be meet will instead be aggregated
-	// into an "overflow" metric stream. That stream will only contain the
-	// "otel.metric.overflow"=true attribute.
-	aggLimit int
-}
-
-// newLimiter returns a new Limiter with the provided aggregation limit.
-func newLimiter[V any](aggregation int) limiter[V] {
-	return limiter[V]{aggLimit: aggregation}
-}
-
-// Attributes checks if adding a measurement for attrs will exceed the
-// aggregation cardinality limit for the existing measurements. If it will,
-// overflowSet is returned. Otherwise, if it will not exceed the limit, or the
-// limit is not set (limit <= 0), attr is returned.
-func (l limiter[V]) Attributes(attrs attribute.Set, measurements map[attribute.Distinct]V) attribute.Set {
-	if l.aggLimit > 0 {
-		_, exists := measurements[attrs.Equivalent()]
-		if !exists && len(measurements) >= l.aggLimit-1 {
-			return overflowSet
-		}
-	}
-
-	return attrs
-}
diff --git a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go b/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go
deleted file mode 100644
index 8e132ad61..000000000
--- a/vendor/go.opentelemetry.io/otel/sdk/metric/internal/aggregate/sum.go
+++ /dev/null
@@ -1,237 +0,0 @@
-// Copyright The OpenTelemetry Authors
-// SPDX-License-Identifier: Apache-2.0
-
-package aggregate // import "go.opentelemetry.io/otel/sdk/metric/internal/aggregate"
-
-import (
-	"context"
-	"sync"
-	"time"
-
-	"go.opentelemetry.io/otel/attribute"
-	"go.opentelemetry.io/otel/sdk/metric/metricdata"
-)
-
-type sumValue[N int64 | float64] struct {
-	n     N
-	res   FilteredExemplarReservoir[N]
-	attrs attribute.Set
-}
-
-// valueMap is the storage for sums.
-type valueMap[N int64 | float64] struct {
-	sync.Mutex
-	newRes func(attribute.Set) FilteredExemplarReservoir[N]
-	limit  limiter[sumValue[N]]
-	values map[attribute.Distinct]sumValue[N]
-}
-
-func newValueMap[N int64 | float64](limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *valueMap[N] {
-	return &valueMap[N]{
-		newRes: r,
-		limit:  newLimiter[sumValue[N]](limit),
-		values: make(map[attribute.Distinct]sumValue[N]),
-	}
-}
-
-func (s *valueMap[N]) measure(ctx context.Context, value N, fltrAttr attribute.Set, droppedAttr []attribute.KeyValue) {
-	s.Lock()
-	defer s.Unlock()
-
-	attr := s.limit.Attributes(fltrAttr, s.values)
-	v, ok := s.values[attr.Equivalent()]
-	if !ok {
-		v.res = s.newRes(attr)
-	}
-
-	v.attrs = attr
-	v.n += value
-	v.res.Offer(ctx, value, droppedAttr)
-
-	s.values[attr.Equivalent()] = v
-}
-
-// newSum returns an aggregator that summarizes a set of measurements as their
-// arithmetic sum. Each sum is scoped by attributes and the aggregation cycle
-// the measurements were made in.
-func newSum[N int64 | float64](monotonic bool, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *sum[N] {
-	return &sum[N]{
-		valueMap:  newValueMap[N](limit, r),
-		monotonic: monotonic,
-		start:     now(),
-	}
-}
-
-// sum summarizes a set of measurements made as their arithmetic sum.
-type sum[N int64 | float64] struct {
-	*valueMap[N]
-
-	monotonic bool
-	start     time.Time
-}
-
-func (s *sum[N]) delta(dest *metricdata.Aggregation) int {
-	t := now()
-
-	// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
-	// use the zero-value sData and hope for better alignment next cycle.
-	sData, _ := (*dest).(metricdata.Sum[N])
-	sData.Temporality = metricdata.DeltaTemporality
-	sData.IsMonotonic = s.monotonic
-
-	s.Lock()
-	defer s.Unlock()
-
-	n := len(s.values)
-	dPts := reset(sData.DataPoints, n, n)
-
-	var i int
-	for _, val := range s.values {
-		dPts[i].Attributes = val.attrs
-		dPts[i].StartTime = s.start
-		dPts[i].Time = t
-		dPts[i].Value = val.n
-		collectExemplars(&dPts[i].Exemplars, val.res.Collect)
-		i++
-	}
-	// Do not report stale values.
-	clear(s.values)
-	// The delta collection cycle resets.
-	s.start = t
-
-	sData.DataPoints = dPts
-	*dest = sData
-
-	return n
-}
-
-func (s *sum[N]) cumulative(dest *metricdata.Aggregation) int {
-	t := now()
-
-	// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
-	// use the zero-value sData and hope for better alignment next cycle.
-	sData, _ := (*dest).(metricdata.Sum[N])
-	sData.Temporality = metricdata.CumulativeTemporality
-	sData.IsMonotonic = s.monotonic
-
-	s.Lock()
-	defer s.Unlock()
-
-	n := len(s.values)
-	dPts := reset(sData.DataPoints, n, n)
-
-	var i int
-	for _, value := range s.values {
-		dPts[i].Attributes = value.attrs
-		dPts[i].StartTime = s.start
-		dPts[i].Time = t
-		dPts[i].Value = value.n
-		collectExemplars(&dPts[i].Exemplars, value.res.Collect)
-		// TODO (#3006): This will use an unbounded amount of memory if there
-		// are unbounded number of attribute sets being aggregated. Attribute
-		// sets that become "stale" need to be forgotten so this will not
-		// overload the system.
-		i++
-	}
-
-	sData.DataPoints = dPts
-	*dest = sData
-
-	return n
-}
-
-// newPrecomputedSum returns an aggregator that summarizes a set of
-// observations as their arithmetic sum. Each sum is scoped by attributes and
-// the aggregation cycle the measurements were made in.
-func newPrecomputedSum[N int64 | float64](monotonic bool, limit int, r func(attribute.Set) FilteredExemplarReservoir[N]) *precomputedSum[N] {
-	return &precomputedSum[N]{
-		valueMap:  newValueMap[N](limit, r),
-		monotonic: monotonic,
-		start:     now(),
-	}
-}
-
-// precomputedSum summarizes a set of observations as their arithmetic sum.
-type precomputedSum[N int64 | float64] struct {
-	*valueMap[N]
-
-	monotonic bool
-	start     time.Time
-
-	reported map[attribute.Distinct]N
-}
-
-func (s *precomputedSum[N]) delta(dest *metricdata.Aggregation) int {
-	t := now()
-	newReported := make(map[attribute.Distinct]N)
-
-	// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
-	// use the zero-value sData and hope for better alignment next cycle.
-	sData, _ := (*dest).(metricdata.Sum[N])
-	sData.Temporality = metricdata.DeltaTemporality
-	sData.IsMonotonic = s.monotonic
-
-	s.Lock()
-	defer s.Unlock()
-
-	n := len(s.values)
-	dPts := reset(sData.DataPoints, n, n)
-
-	var i int
-	for key, value := range s.values {
-		delta := value.n - s.reported[key]
-
-		dPts[i].Attributes = value.attrs
-		dPts[i].StartTime = s.start
-		dPts[i].Time = t
-		dPts[i].Value = delta
-		collectExemplars(&dPts[i].Exemplars, value.res.Collect)
-
-		newReported[key] = value.n
-		i++
-	}
-	// Unused attribute sets do not report.
-	clear(s.values)
-	s.reported = newReported
-	// The delta collection cycle resets.
-	s.start = t
-
-	sData.DataPoints = dPts
-	*dest = sData
-
-	return n
-}
-
-func (s *precomputedSum[N]) cumulative(dest *metricdata.Aggregation) int {
-	t := now()
-
-	// If *dest is not a metricdata.Sum, memory reuse is missed. In that case,
-	// use the zero-value sData and hope for better alignment next cycle.
-	sData, _ := (*dest).(metricdata.Sum[N])
-	sData.Temporality = metricdata.CumulativeTemporality
-	sData.IsMonotonic = s.monotonic
-
-	s.Lock()
-	defer s.Unlock()
-
-	n := len(s.values)
-	dPts := reset(sData.DataPoints, n, n)
-
-	var i int
-	for _, val := range s.values {
-		dPts[i].Attributes = val.attrs
-		dPts[i].StartTime = s.start
-		dPts[i].Time = t
-		dPts[i].Value = val.n
-		collectExemplars(&dPts[i].Exemplars, val.res.Collect)
-
-		i++
-	}
-	// Unused attribute sets do not report.
-	clear(s.values)
-
-	sData.DataPoints = dPts
-	*dest = sData
-
-	return n
-}